Regularization Effect

Regularization techniques aim to improve the generalization ability of machine learning models, preventing overfitting to training data and enhancing performance on unseen data. Current research focuses on developing novel regularization methods, such as variations of dropout, gradient regularization, and data augmentation, often applied within deep neural networks including convolutional neural networks and transformers. These advancements are crucial for improving the reliability and robustness of machine learning models across diverse applications, particularly in scenarios with limited data or noisy labels. A deeper understanding of regularization's effects, including its interplay with optimization algorithms and model architectures, remains a key area of investigation.